import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
				import tensorflow as tf

if is_tensorflow_text_available():
				from transformers.models.bert import TFBertTokenizer


A						=			['bert-base-uncased', 'bert-base-cased']
A						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():





				class     lowerCAmelCase__     (				tf.keras.Model			):


						'''simple docstring'''






						def __init__(       self     :     Optional[int]     ,  snake_case__     :     Union[str, Any]		)      ->	List[str]:
								super().__init__()
								_lowerCamelCase				   =  tokenizer
								_lowerCamelCase				   =  AutoConfig.from_pretrained(snake_case__		)
								_lowerCamelCase				   =  TFAutoModel.from_config(snake_case__		)



						def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]		)      ->	Optional[Any]:
								_lowerCamelCase				   =  self.tokenizer(snake_case__		)
								_lowerCamelCase				   =  self.bert(**snake_case__		)
								return out["pooler_output"]





@require_tf
@require_tensorflow_text
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Union[str, Any]		)      ->	str:
				super().setUp()

				_lowerCamelCase				   =  [
				    BertTokenizer.from_pretrained(snake_case__		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
				]  # repeat for when fast_bert_tokenizer=false
				_lowerCamelCase				   =  [TFBertTokenizer.from_pretrained(snake_case__		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
				    TFBertTokenizer.from_pretrained(snake_case__     ,  use_fast_bert_tokenizer=snake_case__		)
				    for checkpoint in TOKENIZER_CHECKPOINTS
				]
				assert len(self.tokenizers		) == len(self.tf_tokenizers		)

				_lowerCamelCase				   =  [
				    'This is a straightforward English test sentence.',
				    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
				    'Now we\'re going to add some Chinese: 一 二 三 一二三',
				    'And some much more rare Chinese: 齉 堃 齉堃',
				    'Je vais aussi écrire en français pour tester les accents',
				    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
				]
				_lowerCamelCase				   =  list(zip(self.test_sentences     ,  self.test_sentences[::-1]		)		)
		def   _snake_case       (       self     :     Any		)      ->	Optional[int]:
				for tokenizer, tf_tokenizer in zip(self.tokenizers     ,  self.tf_tokenizers		):
						for test_inputs in (self.test_sentences, self.paired_sentences):
								_lowerCamelCase				   =  tokenizer(snake_case__     ,  return_tensors='tf'     ,  padding='longest'		)
								_lowerCamelCase				   =  tf_tokenizer(snake_case__		)

								for key in python_outputs.keys():
										self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
										self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]     ,  tf.intaa		) == tf_outputs[key]		)		)
		@slow
		def   _snake_case       (       self     :     int		)      ->	List[str]:
				for tf_tokenizer in self.tf_tokenizers:
						_lowerCamelCase				   =  tf_tokenizer(self.paired_sentences		)
						_lowerCamelCase				   =  tf_tokenizer(
						    text=[sentence[0] for sentence in self.paired_sentences]     ,  text_pair=[sentence[1] for sentence in self.paired_sentences]     ,  )
						for key in merged_outputs.keys():
								self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]     ,  tf.intaa		) == separated_outputs[key]		)		)
		@slow
		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				for tf_tokenizer in self.tf_tokenizers:
						_lowerCamelCase				   =  tf.function(snake_case__		)
						for test_inputs in (self.test_sentences, self.paired_sentences):
								_lowerCamelCase				   =  tf.constant(snake_case__		)
								_lowerCamelCase				   =  compiled_tokenizer(snake_case__		)
								_lowerCamelCase				   =  tf_tokenizer(snake_case__		)

								for key in eager_outputs.keys():
										self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)



		@slow
		def   _snake_case       (       self     :     int		)      ->	int:
				for tf_tokenizer in self.tf_tokenizers:
						_lowerCamelCase				   =  ModelToSave(tokenizer=snake_case__		)
						_lowerCamelCase				   =  tf.convert_to_tensor(self.test_sentences		)
						_lowerCamelCase				   =  model(snake_case__		)  # Build model with some sample inputs
						with TemporaryDirectory() as tempdir:
								_lowerCamelCase				   =  Path(snake_case__		) / 'saved.model'
								model.save(snake_case__		)
								_lowerCamelCase				   =  tf.keras.models.load_model(snake_case__		)
						_lowerCamelCase				   =  loaded_model(snake_case__		)
						# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
						self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)     ,  1e-5		)
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    float:
		return base * power(UpperCamelCase			,       (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
				print('Raise base to the power of exponent using recursion...')
				A						=			int(input('Enter the base: ').strip())
				A						=			int(input('Enter the exponent: ').strip())
				A						=			power(base, abs(exponent))
				if exponent < 0:  # power() does not properly deal w/ negative exponents
								A						=			1 / result
				print(F'''{base} to the power of {exponent} is {result}''')